A survey of clustering algorithms for big data

Taxonomy and empirical analysis

Adil Fahad, Najlaa Alshatri, Zahir Tari, Abdullah Alamri, Ibrahim Khalil, Albert Y. Zomaya, Sebti Foufou, Abdelaziz Bouras

    Research output: Contribution to journalArticle

    Abstract

    Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

    Original languageEnglish (US)
    Article number6832486
    Pages (from-to)267-279
    Number of pages13
    JournalIEEE Transactions on Emerging Topics in Computing
    Volume2
    Issue number3
    DOIs
    StatePublished - Jan 1 2014

    Fingerprint

    Taxonomies
    Clustering algorithms
    Scalability
    Big data
    Experiments

    Keywords

    • big data
    • Clustering algorithms
    • unsupervised learning

    ASJC Scopus subject areas

    • Computer Science (miscellaneous)
    • Information Systems
    • Human-Computer Interaction
    • Computer Science Applications

    Cite this

    Fahad, A., Alshatri, N., Tari, Z., Alamri, A., Khalil, I., Zomaya, A. Y., ... Bouras, A. (2014). A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Transactions on Emerging Topics in Computing, 2(3), 267-279. [6832486]. https://doi.org/10.1109/TETC.2014.2330519

    A survey of clustering algorithms for big data : Taxonomy and empirical analysis. / Fahad, Adil; Alshatri, Najlaa; Tari, Zahir; Alamri, Abdullah; Khalil, Ibrahim; Zomaya, Albert Y.; Foufou, Sebti; Bouras, Abdelaziz.

    In: IEEE Transactions on Emerging Topics in Computing, Vol. 2, No. 3, 6832486, 01.01.2014, p. 267-279.

    Research output: Contribution to journalArticle

    Fahad, A, Alshatri, N, Tari, Z, Alamri, A, Khalil, I, Zomaya, AY, Foufou, S & Bouras, A 2014, 'A survey of clustering algorithms for big data: Taxonomy and empirical analysis', IEEE Transactions on Emerging Topics in Computing, vol. 2, no. 3, 6832486, pp. 267-279. https://doi.org/10.1109/TETC.2014.2330519
    Fahad, Adil ; Alshatri, Najlaa ; Tari, Zahir ; Alamri, Abdullah ; Khalil, Ibrahim ; Zomaya, Albert Y. ; Foufou, Sebti ; Bouras, Abdelaziz. / A survey of clustering algorithms for big data : Taxonomy and empirical analysis. In: IEEE Transactions on Emerging Topics in Computing. 2014 ; Vol. 2, No. 3. pp. 267-279.
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